This project aims to investigate the feasibility of using mixed observationtypes in Hidden Markov Models, in an attampt to increase the accuracyof recognising strategies, and predicting future actions in the domain of thereal-time strategy game StarCraft. The types of observations in the modelwill be multinomial and Poisson distributions, and theory for both types ofvariables will be presented, as will theory for the combination of the two.The data for training the model in the StarCraft domain will be analysed todetermine the validity of applyig Poisson distributions to the production ofcombat units. Finally, experiments will be made to determine the accuracyof predictions made by the model, as well as an evaluation of the most likelypath through the state space of the model.